Intelligent Surveillance for Abnormal Behaviour Recognition Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul Gippsland School of Information Technology, Monash University, Victoria 3842, Australia Email: {Mahfuzul.Haque, Manzur.Murshed, Manoranjan.Paul}@infotech.monash.edu.au

1 Abstract

2 Background Modelling

3 Foreground Region Detection

4 Movement Trajectory Computation

5 Behaviour Recognition

6 Performance Evaluation

8 References

7 The Complete System

The images shown in the header and in the middle of the poster have been taken from http://www.informationliberation.com and

http://www.defenestrator.org/drp/files/surveillance-cameras-400.jpg respectively.

[1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background

Generation Technique using Gaussian Mixture Models for Robust Object Detection, to be

appear in IEEE International Conference On Advanced Video and Signal Based Surveillance

(AVSS), New Mexico, USA, 2008.

[2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, A Hybrid Object Detection

Technique from Dynamic Background Using Gaussian Mixture Models, to be appear in IEEE

International Workshop on Multimedia Signal Processing (MMSP), Cairns, Australia, 2008.

[3] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for

Robust Object Detection by Adaptive Multi-Background Generation, to be appear in

International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008.

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Gaussian Mixture Model (GMM)

for each pixel

Input scenes

A pixel model is constructed and updated for each pixel which

maintains a mixture of Gaussian distributions for modelling multi-

modal distribution caused by moving foregrounds and repetitive

background motions [1-3].

Background

Model

Current Scene Foreground Region

Growing number surveillance camera is challenging the reliability of existing surveillance system which is still relying on

human monitors. This project aims to develop a real-time behaviour recognition framework for identifying unusual group

behaviours from surveillance video stream in order to aid human monitors taking early actions against possible malicious

activities. Incoming video stream passes through several complex processes performed by different components of the

framework for high level recognition.